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Predicting Microbial Interactions by Using Network-constrained Regularization Incorporating Covariate Coefficients and Connection Signs

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成果类型:
期刊论文、会议论文
作者:
Wang, Yan*;Hu, Xiaohua;Jiang, Xingpeng(蒋兴鹏);He, Tingting(何婷婷);Yuan, Jie
通讯作者:
Wang, Yan
作者机构:
[Wang, Yan] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
[Jiang, Xingpeng; He, Tingting; Hu, Xiaohua; Yuan, Jie] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
通讯机构:
[Wang, Yan] C
Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
Coordinate descent;Microbial interactions;Microbiome;Time series analysis;Vector autoregression model
期刊:
PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE
ISSN:
2156-1125
年:
2015
页码:
635-638
会议名称:
IEEE International Conference on Bioinformatics and Biomedicine - Medical Informatics and Decision Making
会议论文集名称:
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
会议时间:
NOV 09-12, 2015
会议地点:
Washington, DC
会议主办单位:
[Wang, Yan] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.^[Hu, Xiaohua;Jiang, Xingpeng;He, Tingting;Yuan, Jie] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
会议赞助商:
IEEE, IEEE Comp Soc, Natl Sci Fdn
主编:
Huan, J Miyano, S Shehu, A Hu, X Ma, B Rajasekaran, S Gombar, VK Schapranow, IM Yoo, IH Zhou, JY Chen, B Pai, V Pierce, B
出版地:
345 E 47TH ST, NEW YORK, NY 10017 USA
出版者:
IEEE
ISBN:
978-1-4673-6798-1
机构署名:
本校为第一且通讯机构
院系归属:
计算机学院
国家数字化学习工程技术研究中心
摘要:
Network is an exceptional way of depicting biological information. In biology, many different biological processes are represented by network, such as regulatory network, metabolic network and food web. In biology, network is a powerful Supplement to the standard numerical data such as profile or count data. By absorbing network information, Vector autoregressive (VAR) model was proved to be an efficient approach to infer dynamic interactions in biological systems. Variants of network-regularized VAR with different penalties or regularization c...

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